Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations966400
Missing cells0
Missing cells (%)0.0%
Duplicate rows3158
Duplicate rows (%)0.3%
Total size in memory645.0 MiB
Average record size in memory699.8 B

Variable types

Numeric6
DateTime2
Categorical6
Text4

Alerts

Dataset has 3158 (0.3%) duplicate rowsDuplicates
CATEGORY is highly overall correlated with SUBCATEGORYHigh correlation
SALES_PTR_VALUE is highly overall correlated with SALES_VALUE and 1 other fieldsHigh correlation
SALES_VALUE is highly overall correlated with SALES_PTR_VALUE and 1 other fieldsHigh correlation
SALES_VOLUME is highly overall correlated with SALES_PTR_VALUE and 1 other fieldsHigh correlation
SUBCATEGORY is highly overall correlated with CATEGORYHigh correlation
SALES_VALUE is highly skewed (γ1 = 33.86988697)Skewed
SALES_UNITS is highly skewed (γ1 = 57.47378806)Skewed
SALES_VOLUME is highly skewed (γ1 = 34.22440987)Skewed
SALES_PTR_VALUE is highly skewed (γ1 = 32.65774068)Skewed

Reproduction

Analysis started2024-09-25 06:46:04.786614
Analysis finished2024-09-25 06:46:37.577868
Duration32.79 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

MNTH_CODE
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202372.88
Minimum202309
Maximum202408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-09-25T06:46:37.664584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum202309
5-th percentile202309
Q1202312
median202403
Q3202406
95-th percentile202408
Maximum202408
Range99
Interquartile range (IQR)94

Descriptive statistics

Standard deviation44.525843
Coefficient of variation (CV)0.00022001883
Kurtosis-1.5218993
Mean202372.88
Median Absolute Deviation (MAD)4
Skewness-0.68520023
Sum1.9557315 × 1011
Variance1982.5507
MonotonicityNot monotonic
2024-09-25T06:46:37.822873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
202406 102585
10.6%
202309 96453
10.0%
202312 89583
9.3%
202403 84599
8.8%
202407 83813
8.7%
202408 74960
7.8%
202404 74939
7.8%
202402 74173
7.7%
202311 73399
7.6%
202401 73094
7.6%
Other values (2) 138802
14.4%
ValueCountFrequency (%)
202309 96453
10.0%
202310 66211
6.9%
202311 73399
7.6%
202312 89583
9.3%
202401 73094
7.6%
202402 74173
7.7%
202403 84599
8.8%
202404 74939
7.8%
202405 72591
7.5%
202406 102585
10.6%
ValueCountFrequency (%)
202408 74960
7.8%
202407 83813
8.7%
202406 102585
10.6%
202405 72591
7.5%
202404 74939
7.8%
202403 84599
8.8%
202402 74173
7.7%
202401 73094
7.6%
202312 89583
9.3%
202311 73399
7.6%
Distinct303
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
Minimum2023-08-29 00:00:00
Maximum2024-08-27 00:00:00
2024-09-25T06:46:37.989187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:38.178409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
Minimum2023-08-28 00:00:00
Maximum2024-07-31 00:00:00
2024-09-25T06:46:38.329088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:38.423603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

SALES_VALUE
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8242
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446.41745
Minimum2.86
Maximum145728.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2024-09-25T06:46:38.537110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.86
5-th percentile53.57
Q1140
median192.24
Q3450
95-th percentile1537.89
Maximum145728.12
Range145725.26
Interquartile range (IQR)310

Descriptive statistics

Standard deviation1053.3556
Coefficient of variation (CV)2.3595754
Kurtosis2647.1191
Mean446.41745
Median Absolute Deviation (MAD)87.76
Skewness33.869887
Sum4.3141783 × 108
Variance1109558.1
MonotonicityNot monotonic
2024-09-25T06:46:38.670442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142.86 106972
 
11.1%
53.57 42760
 
4.4%
140 39597
 
4.1%
138.57 30170
 
3.1%
107.14 28309
 
2.9%
137.14 24945
 
2.6%
142.84 21903
 
2.3%
280 12096
 
1.3%
131.06 10428
 
1.1%
163.64 10138
 
1.0%
Other values (8232) 639082
66.1%
ValueCountFrequency (%)
2.86 2
 
< 0.1%
4.46 42
 
< 0.1%
7.81 1
 
< 0.1%
8.57 10
 
< 0.1%
8.65 1
 
< 0.1%
8.66 13
 
< 0.1%
8.75 16
 
< 0.1%
8.92 1
 
< 0.1%
8.93 254
< 0.1%
13.39 84
 
< 0.1%
ValueCountFrequency (%)
145728.12 1
< 0.1%
144803.75 1
< 0.1%
118027.64 1
< 0.1%
117676.65 1
< 0.1%
114606.54 1
< 0.1%
112931 1
< 0.1%
104407.27 1
< 0.1%
103488 1
< 0.1%
96727.27 1
< 0.1%
95594.54 1
< 0.1%

SALES_UNITS
Real number (ℝ)

SKEWED 

Distinct359
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.696476
Minimum1
Maximum10240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-09-25T06:46:38.803530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q316
95-th percentile32
Maximum10240
Range10239
Interquartile range (IQR)14

Descriptive statistics

Standard deviation40.761336
Coefficient of variation (CV)3.210445
Kurtosis7131.9482
Mean12.696476
Median Absolute Deviation (MAD)5
Skewness57.473788
Sum12269874
Variance1661.4865
MonotonicityNot monotonic
2024-09-25T06:46:38.940243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 260404
26.9%
1 131257
13.6%
2 122969
12.7%
3 119386
12.4%
6 72771
 
7.5%
12 71214
 
7.4%
32 57991
 
6.0%
4 34619
 
3.6%
24 25479
 
2.6%
8 17878
 
1.8%
Other values (349) 52432
 
5.4%
ValueCountFrequency (%)
1 131257
13.6%
2 122969
12.7%
3 119386
12.4%
4 34619
 
3.6%
5 5173
 
0.5%
6 72771
7.5%
7 579
 
0.1%
8 17878
 
1.8%
9 601
 
0.1%
10 2070
 
0.2%
ValueCountFrequency (%)
10240 1
 
< 0.1%
6000 1
 
< 0.1%
5120 2
< 0.1%
4800 1
 
< 0.1%
4388 1
 
< 0.1%
3840 1
 
< 0.1%
3600 3
< 0.1%
3500 1
 
< 0.1%
3360 1
 
< 0.1%
3200 1
 
< 0.1%

SALES_VOLUME
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1581
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00093144967
Minimum1.1 × 10-5
Maximum0.2755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2024-09-25T06:46:39.062983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 10-5
5-th percentile0.000144
Q10.000368
median0.000448
Q30.0009
95-th percentile0.00286
Maximum0.2755
Range0.275489
Interquartile range (IQR)0.000532

Descriptive statistics

Standard deviation0.0020629814
Coefficient of variation (CV)2.2148071
Kurtosis2628.5773
Mean0.00093144967
Median Absolute Deviation (MAD)0.000198
Skewness34.22441
Sum900.15296
Variance4.2558922 × 10-6
MonotonicityNot monotonic
2024-09-25T06:46:39.191922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.000384 67198
 
7.0%
0.0004 63048
 
6.5%
0.000416 44261
 
4.6%
0.0005 32689
 
3.4%
0.000144 32367
 
3.3%
0.0003 25749
 
2.7%
0.00075 24511
 
2.5%
0.000368 22589
 
2.3%
0.000272 21633
 
2.2%
0.000132 20296
 
2.1%
Other values (1571) 612059
63.3%
ValueCountFrequency (%)
1.1 × 10-56
 
< 0.1%
1.2 × 10-536
 
< 0.1%
1.7 × 10-517
 
< 0.1%
1.8 × 10-56
 
< 0.1%
2 × 10-57
 
< 0.1%
2.2 × 10-522
 
< 0.1%
2.3 × 10-533
 
< 0.1%
2.4 × 10-5106
< 0.1%
2.5 × 10-547
< 0.1%
2.6 × 10-550
< 0.1%
ValueCountFrequency (%)
0.2755 1
 
< 0.1%
0.256 1
 
< 0.1%
0.2464 1
 
< 0.1%
0.242 1
 
< 0.1%
0.2375 1
 
< 0.1%
0.219 1
 
< 0.1%
0.21 1
 
< 0.1%
0.2097 1
 
< 0.1%
0.2095 1
 
< 0.1%
0.192 3
< 0.1%

SALES_PTR_VALUE
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2024
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean457.81468
Minimum1.7857143
Maximum151800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2024-09-25T06:46:39.472289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.7857143
5-th percentile53.571429
Q1142.85714
median198.18182
Q3450
95-th percentile1585.4545
Maximum151800
Range151798.21
Interquartile range (IQR)307.14286

Descriptive statistics

Standard deviation1101.7691
Coefficient of variation (CV)2.4065832
Kurtosis2447.2998
Mean457.81468
Median Absolute Deviation (MAD)91.038961
Skewness32.657741
Sum4.4243211 × 108
Variance1213895.2
MonotonicityNot monotonic
2024-09-25T06:46:39.613805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142.8571429 239443
24.8%
53.57142857 52969
 
5.5%
285.7142857 50215
 
5.2%
107.1428571 33816
 
3.5%
163.6363636 10338
 
1.1%
313.6363636 9855
 
1.0%
336.3636364 9789
 
1.0%
270 9751
 
1.0%
104.5454545 9638
 
1.0%
209.0909091 9229
 
1.0%
Other values (2014) 531357
55.0%
ValueCountFrequency (%)
1.785714286 1
 
< 0.1%
4.464285714 42
 
< 0.1%
8.035714286 6
 
< 0.1%
8.928571429 290
< 0.1%
13.39285714 84
 
< 0.1%
16.07142857 5
 
< 0.1%
17.85714286 260
< 0.1%
22.32142857 15
 
< 0.1%
24.10714286 3
 
< 0.1%
26.78571429 90
 
< 0.1%
ValueCountFrequency (%)
151800 1
< 0.1%
144659.0909 1
< 0.1%
120436.3636 1
< 0.1%
117559.0909 1
< 0.1%
116945.4545 1
< 0.1%
112818.1818 1
< 0.1%
107636.3636 1
< 0.1%
105600 1
< 0.1%
97545.45455 1
< 0.1%
97454.54545 2
< 0.1%

OC_CODE
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202206.57
Minimum202201
Maximum202212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-09-25T06:46:39.728462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum202201
5-th percentile202201
Q1202204
median202207
Q3202209
95-th percentile202212
Maximum202212
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4045263
Coefficient of variation (CV)1.6836873 × 10-5
Kurtosis-1.1578826
Mean202206.57
Median Absolute Deviation (MAD)3
Skewness-0.0061898714
Sum1.9541243 × 1011
Variance11.590799
MonotonicityNot monotonic
2024-09-25T06:46:39.830523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
202206 102585
10.6%
202209 96453
10.0%
202212 89583
9.3%
202203 84599
8.8%
202207 83813
8.7%
202208 74960
7.8%
202204 74939
7.8%
202202 74173
7.7%
202211 73399
7.6%
202201 73094
7.6%
Other values (2) 138802
14.4%
ValueCountFrequency (%)
202201 73094
7.6%
202202 74173
7.7%
202203 84599
8.8%
202204 74939
7.8%
202205 72591
7.5%
202206 102585
10.6%
202207 83813
8.7%
202208 74960
7.8%
202209 96453
10.0%
202210 66211
6.9%
ValueCountFrequency (%)
202212 89583
9.3%
202211 73399
7.6%
202210 66211
6.9%
202209 96453
10.0%
202208 74960
7.8%
202207 83813
8.7%
202206 102585
10.6%
202205 72591
7.5%
202204 74939
7.8%
202203 84599
8.8%

DISTRIBUTOR_CODE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.1 MiB
DB0110
278245 
DB0209
217421 
DB0706
194044 
DB0652
142181 
DB0655
134509 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters5798400
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDB0209
2nd rowDB0706
3rd rowDB0209
4th rowDB0209
5th rowDB0209

Common Values

ValueCountFrequency (%)
DB0110 278245
28.8%
DB0209 217421
22.5%
DB0706 194044
20.1%
DB0652 142181
14.7%
DB0655 134509
13.9%

Length

2024-09-25T06:46:39.954688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-25T06:46:40.052473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
db0110 278245
28.8%
db0209 217421
22.5%
db0706 194044
20.1%
db0652 142181
14.7%
db0655 134509
13.9%

Most occurring characters

ValueCountFrequency (%)
0 1656110
28.6%
D 966400
16.7%
B 966400
16.7%
1 556490
 
9.6%
6 470734
 
8.1%
5 411199
 
7.1%
2 359602
 
6.2%
9 217421
 
3.7%
7 194044
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5798400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1656110
28.6%
D 966400
16.7%
B 966400
16.7%
1 556490
 
9.6%
6 470734
 
8.1%
5 411199
 
7.1%
2 359602
 
6.2%
9 217421
 
3.7%
7 194044
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5798400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1656110
28.6%
D 966400
16.7%
B 966400
16.7%
1 556490
 
9.6%
6 470734
 
8.1%
5 411199
 
7.1%
2 359602
 
6.2%
9 217421
 
3.7%
7 194044
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5798400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1656110
28.6%
D 966400
16.7%
B 966400
16.7%
1 556490
 
9.6%
6 470734
 
8.1%
5 411199
 
7.1%
2 359602
 
6.2%
9 217421
 
3.7%
7 194044
 
3.3%
Distinct18833
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size59.5 MiB
2024-09-25T06:46:40.309931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.5882212
Min length7

Characters and Unicode

Total characters7333257
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique382 ?
Unique (%)< 0.1%

Sample

1st rowOL12036
2nd rowOL49989
3rd rowOL112160
4th rowOL175188
5th rowOL80360
ValueCountFrequency (%)
ol128896 1289
 
0.1%
ol191061 1277
 
0.1%
ol49938 1243
 
0.1%
ol143966 1223
 
0.1%
ol11104 1114
 
0.1%
ol223486 1089
 
0.1%
ol191033 1085
 
0.1%
ol32854 1080
 
0.1%
ol80887 1048
 
0.1%
ol159815 1035
 
0.1%
Other values (18823) 954917
98.8%
2024-09-25T06:46:40.652448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 966400
13.2%
L 966400
13.2%
1 879608
12.0%
2 729852
10.0%
3 514930
7.0%
9 513662
7.0%
4 494825
6.7%
6 477333
6.5%
5 467004
6.4%
8 459480
6.3%
Other values (2) 863763
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7333257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 966400
13.2%
L 966400
13.2%
1 879608
12.0%
2 729852
10.0%
3 514930
7.0%
9 513662
7.0%
4 494825
6.7%
6 477333
6.5%
5 467004
6.4%
8 459480
6.3%
Other values (2) 863763
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7333257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 966400
13.2%
L 966400
13.2%
1 879608
12.0%
2 729852
10.0%
3 514930
7.0%
9 513662
7.0%
4 494825
6.7%
6 477333
6.5%
5 467004
6.4%
8 459480
6.3%
Other values (2) 863763
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7333257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 966400
13.2%
L 966400
13.2%
1 879608
12.0%
2 729852
10.0%
3 514930
7.0%
9 513662
7.0%
4 494825
6.7%
6 477333
6.5%
5 467004
6.4%
8 459480
6.3%
Other values (2) 863763
11.8%

CITY
Text

Distinct1679
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.6 MiB
2024-09-25T06:46:40.894688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length8.7443057
Min length3

Characters and Unicode

Total characters8450497
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWauwatosa
2nd rowHuntington
3rd rowSaint Augustine
4th rowRedwood City
5th rowKokomo
ValueCountFrequency (%)
city 32161
 
2.6%
new 14047
 
1.2%
beach 13726
 
1.1%
san 12949
 
1.1%
springs 10660
 
0.9%
fort 10024
 
0.8%
park 9132
 
0.8%
west 8548
 
0.7%
saint 7971
 
0.7%
falls 6906
 
0.6%
Other values (1634) 1090751
89.6%
2024-09-25T06:46:41.348155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 777071
 
9.2%
e 776788
 
9.2%
n 643293
 
7.6%
o 634555
 
7.5%
r 534447
 
6.3%
l 513454
 
6.1%
i 509793
 
6.0%
t 467095
 
5.5%
s 353589
 
4.2%
250475
 
3.0%
Other values (44) 2989937
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8450497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 777071
 
9.2%
e 776788
 
9.2%
n 643293
 
7.6%
o 634555
 
7.5%
r 534447
 
6.3%
l 513454
 
6.1%
i 509793
 
6.0%
t 467095
 
5.5%
s 353589
 
4.2%
250475
 
3.0%
Other values (44) 2989937
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8450497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 777071
 
9.2%
e 776788
 
9.2%
n 643293
 
7.6%
o 634555
 
7.5%
r 534447
 
6.3%
l 513454
 
6.1%
i 509793
 
6.0%
t 467095
 
5.5%
s 353589
 
4.2%
250475
 
3.0%
Other values (44) 2989937
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8450497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 777071
 
9.2%
e 776788
 
9.2%
n 643293
 
7.6%
o 634555
 
7.5%
r 534447
 
6.3%
l 513454
 
6.1%
i 509793
 
6.0%
t 467095
 
5.5%
s 353589
 
4.2%
250475
 
3.0%
Other values (44) 2989937
35.4%

STATE
Categorical

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.4 MiB
California
111427 
Illinois
 
56753
Massachusetts
 
47597
Connecticut
 
41359
New York
 
39138
Other values (45)
670126 

Length

Max length14
Median length12
Mean length8.5429739
Min length4

Characters and Unicode

Total characters8255930
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWisconsin
2nd rowWest Virginia
3rd rowFlorida
4th rowCalifornia
5th rowIndiana

Common Values

ValueCountFrequency (%)
California 111427
 
11.5%
Illinois 56753
 
5.9%
Massachusetts 47597
 
4.9%
Connecticut 41359
 
4.3%
New York 39138
 
4.0%
Alabama 38935
 
4.0%
Florida 38350
 
4.0%
Colorado 30124
 
3.1%
New Jersey 26100
 
2.7%
Arkansas 25677
 
2.7%
Other values (40) 510940
52.9%

Length

2024-09-25T06:46:41.476444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 111427
 
10.1%
new 84237
 
7.6%
illinois 56753
 
5.1%
massachusetts 47597
 
4.3%
connecticut 41359
 
3.8%
york 39138
 
3.6%
alabama 38935
 
3.5%
florida 38350
 
3.5%
colorado 30124
 
2.7%
jersey 26100
 
2.4%
Other values (42) 588071
53.4%

Most occurring characters

ValueCountFrequency (%)
a 1132803
13.7%
i 865262
 
10.5%
n 692063
 
8.4%
o 663356
 
8.0%
s 604292
 
7.3%
e 451896
 
5.5%
r 443948
 
5.4%
l 435199
 
5.3%
t 293046
 
3.5%
C 205185
 
2.5%
Other values (36) 2468880
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8255930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1132803
13.7%
i 865262
 
10.5%
n 692063
 
8.4%
o 663356
 
8.0%
s 604292
 
7.3%
e 451896
 
5.5%
r 443948
 
5.4%
l 435199
 
5.3%
t 293046
 
3.5%
C 205185
 
2.5%
Other values (36) 2468880
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8255930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1132803
13.7%
i 865262
 
10.5%
n 692063
 
8.4%
o 663356
 
8.0%
s 604292
 
7.3%
e 451896
 
5.5%
r 443948
 
5.4%
l 435199
 
5.3%
t 293046
 
3.5%
C 205185
 
2.5%
Other values (36) 2468880
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8255930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1132803
13.7%
i 865262
 
10.5%
n 692063
 
8.4%
o 663356
 
8.0%
s 604292
 
7.3%
e 451896
 
5.5%
r 443948
 
5.4%
l 435199
 
5.3%
t 293046
 
3.5%
C 205185
 
2.5%
Other values (36) 2468880
29.9%

COUNTY
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.9 MiB
City Center
507296 
Dolphin
148154 
Orange
86564 
Santa Cruz
63187 
Scott
50866 
Other values (4)
110333 

Length

Max length11
Median length11
Mean length9.0649369
Min length5

Characters and Unicode

Total characters8760355
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScott
2nd rowCity Center
3rd rowCity Center
4th rowCity Center
5th rowCity Center

Common Values

ValueCountFrequency (%)
City Center 507296
52.5%
Dolphin 148154
 
15.3%
Orange 86564
 
9.0%
Santa Cruz 63187
 
6.5%
Scott 50866
 
5.3%
Silver 40951
 
4.2%
Spencer 31985
 
3.3%
Stephens 21727
 
2.2%
Sumter 15670
 
1.6%

Length

2024-09-25T06:46:41.595052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-25T06:46:41.706517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
city 507296
33.0%
center 507296
33.0%
dolphin 148154
 
9.6%
orange 86564
 
5.6%
santa 63187
 
4.1%
cruz 63187
 
4.1%
scott 50866
 
3.3%
silver 40951
 
2.7%
spencer 31985
 
2.1%
stephens 21727
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 1265201
14.4%
t 1216908
13.9%
C 1077779
12.3%
n 858913
9.8%
r 745653
8.5%
i 696401
7.9%
570483
6.5%
y 507296
 
5.8%
S 224386
 
2.6%
a 212938
 
2.4%
Other values (13) 1384397
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8760355
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1265201
14.4%
t 1216908
13.9%
C 1077779
12.3%
n 858913
9.8%
r 745653
8.5%
i 696401
7.9%
570483
6.5%
y 507296
 
5.8%
S 224386
 
2.6%
a 212938
 
2.4%
Other values (13) 1384397
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8760355
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1265201
14.4%
t 1216908
13.9%
C 1077779
12.3%
n 858913
9.8%
r 745653
8.5%
i 696401
7.9%
570483
6.5%
y 507296
 
5.8%
S 224386
 
2.6%
a 212938
 
2.4%
Other values (13) 1384397
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8760355
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1265201
14.4%
t 1216908
13.9%
C 1077779
12.3%
n 858913
9.8%
r 745653
8.5%
i 696401
7.9%
570483
6.5%
y 507296
 
5.8%
S 224386
 
2.6%
a 212938
 
2.4%
Other values (13) 1384397
15.8%

STREET
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.2 MiB
Str1
201506 
Str4
198939 
Str2
194809 
Str5
194441 
Str3
176705 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3865600
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStr3
2nd rowStr3
3rd rowStr2
4th rowStr5
5th rowStr4

Common Values

ValueCountFrequency (%)
Str1 201506
20.9%
Str4 198939
20.6%
Str2 194809
20.2%
Str5 194441
20.1%
Str3 176705
18.3%

Length

2024-09-25T06:46:41.836217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-25T06:46:41.967028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
str1 201506
20.9%
str4 198939
20.6%
str2 194809
20.2%
str5 194441
20.1%
str3 176705
18.3%

Most occurring characters

ValueCountFrequency (%)
S 966400
25.0%
t 966400
25.0%
r 966400
25.0%
1 201506
 
5.2%
4 198939
 
5.1%
2 194809
 
5.0%
5 194441
 
5.0%
3 176705
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3865600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 966400
25.0%
t 966400
25.0%
r 966400
25.0%
1 201506
 
5.2%
4 198939
 
5.1%
2 194809
 
5.0%
5 194441
 
5.0%
3 176705
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3865600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 966400
25.0%
t 966400
25.0%
r 966400
25.0%
1 201506
 
5.2%
4 198939
 
5.1%
2 194809
 
5.0%
5 194441
 
5.0%
3 176705
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3865600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 966400
25.0%
t 966400
25.0%
r 966400
25.0%
1 201506
 
5.2%
4 198939
 
5.1%
2 194809
 
5.0%
5 194441
 
5.0%
3 176705
 
4.6%
Distinct94
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
2024-09-25T06:46:42.203095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6764800
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPRD0147
2nd rowPRD0016
3rd rowPRD0118
4th rowPRD0079
5th rowPRD0080
ValueCountFrequency (%)
prd0106 107597
 
11.1%
prd0105 51482
 
5.3%
prd0147 43794
 
4.5%
prd0069 33926
 
3.5%
prd0058 31868
 
3.3%
prd0094 30319
 
3.1%
prd0015 29173
 
3.0%
prd0112 27877
 
2.9%
prd0107 26910
 
2.8%
prd0096 26421
 
2.7%
Other values (84) 557033
57.6%
2024-09-25T06:46:42.499409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1736312
25.7%
P 966400
14.3%
R 966400
14.3%
D 966400
14.3%
1 641564
 
9.5%
6 290649
 
4.3%
5 230585
 
3.4%
9 202083
 
3.0%
2 187942
 
2.8%
8 156096
 
2.3%
Other values (3) 420369
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6764800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1736312
25.7%
P 966400
14.3%
R 966400
14.3%
D 966400
14.3%
1 641564
 
9.5%
6 290649
 
4.3%
5 230585
 
3.4%
9 202083
 
3.0%
2 187942
 
2.8%
8 156096
 
2.3%
Other values (3) 420369
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6764800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1736312
25.7%
P 966400
14.3%
R 966400
14.3%
D 966400
14.3%
1 641564
 
9.5%
6 290649
 
4.3%
5 230585
 
3.4%
9 202083
 
3.0%
2 187942
 
2.8%
8 156096
 
2.3%
Other values (3) 420369
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6764800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1736312
25.7%
P 966400
14.3%
R 966400
14.3%
D 966400
14.3%
1 641564
 
9.5%
6 290649
 
4.3%
5 230585
 
3.4%
9 202083
 
3.0%
2 187942
 
2.8%
8 156096
 
2.3%
Other values (3) 420369
 
6.2%

CATEGORY
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.7 MiB
Soap
251031 
Perfume and Deodrants
224223 
Hair Care
203882 
Lotion
138579 
Kids Care
101069 
Other values (2)
47616 

Length

Max length21
Median length9
Mean length9.9073489
Min length4

Characters and Unicode

Total characters9574462
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKids Care
2nd rowHair Care
3rd rowSoap
4th rowPerfume and Deodrants
5th rowPerfume and Deodrants

Common Values

ValueCountFrequency (%)
Soap 251031
26.0%
Perfume and Deodrants 224223
23.2%
Hair Care 203882
21.1%
Lotion 138579
14.3%
Kids Care 101069
10.5%
Dental 47542
 
4.9%
Wipes 74
 
< 0.1%

Length

2024-09-25T06:46:42.633062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-25T06:46:42.740590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
care 304951
17.7%
soap 251031
14.6%
perfume 224223
13.0%
and 224223
13.0%
deodrants 224223
13.0%
hair 203882
11.9%
lotion 138579
8.1%
kids 101069
 
5.9%
dental 47542
 
2.8%
wipes 74
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 1255852
13.1%
e 1025236
10.7%
r 957279
 
10.0%
753397
 
7.9%
o 752412
 
7.9%
n 634567
 
6.6%
d 549515
 
5.7%
i 443604
 
4.6%
t 410344
 
4.3%
s 325366
 
3.4%
Other values (13) 2466890
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9574462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1255852
13.1%
e 1025236
10.7%
r 957279
 
10.0%
753397
 
7.9%
o 752412
 
7.9%
n 634567
 
6.6%
d 549515
 
5.7%
i 443604
 
4.6%
t 410344
 
4.3%
s 325366
 
3.4%
Other values (13) 2466890
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9574462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1255852
13.1%
e 1025236
10.7%
r 957279
 
10.0%
753397
 
7.9%
o 752412
 
7.9%
n 634567
 
6.6%
d 549515
 
5.7%
i 443604
 
4.6%
t 410344
 
4.3%
s 325366
 
3.4%
Other values (13) 2466890
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9574462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1255852
13.1%
e 1025236
10.7%
r 957279
 
10.0%
753397
 
7.9%
o 752412
 
7.9%
n 634567
 
6.6%
d 549515
 
5.7%
i 443604
 
4.6%
t 410344
 
4.3%
s 325366
 
3.4%
Other values (13) 2466890
25.8%

SUBCATEGORY
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.6 MiB
Shampoo
123413 
Head Lotion
82722 
Soap Gels
68794 
Toilet Soap
68763 
Female Perfume
60317 
Other values (20)
562391 

Length

Max length15
Median length13
Mean length10.878238
Min length3

Characters and Unicode

Total characters10512729
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBaby Cream
2nd rowHair Oil
3rd rowMedicated Soap
4th rowMale Perfume
5th rowUnisex Perfume

Common Values

ValueCountFrequency (%)
Shampoo 123413
12.8%
Head Lotion 82722
 
8.6%
Soap Gels 68794
 
7.1%
Toilet Soap 68763
 
7.1%
Female Perfume 60317
 
6.2%
Female Deodrant 58640
 
6.1%
Body Lotion 55857
 
5.8%
Liquid Soap 50112
 
5.2%
Hair Oil 48057
 
5.0%
Medicated Soap 46940
 
4.9%
Other values (15) 302785
31.3%

Length

2024-09-25T06:46:42.873107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
soap 251031
14.8%
shampoo 151515
 
8.9%
lotion 138579
 
8.2%
perfume 125338
 
7.4%
female 118957
 
7.0%
deodrant 98885
 
5.8%
head 82722
 
4.9%
baby 71896
 
4.2%
gels 68794
 
4.0%
toilet 68763
 
4.0%
Other values (17) 523706
30.8%

Most occurring characters

ValueCountFrequency (%)
o 1214507
 
11.6%
e 1169659
 
11.1%
a 1037431
 
9.9%
733786
 
7.0%
i 612885
 
5.8%
t 490496
 
4.7%
m 439604
 
4.2%
p 431719
 
4.1%
d 413868
 
3.9%
S 402546
 
3.8%
Other values (26) 3566228
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10512729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1214507
 
11.6%
e 1169659
 
11.1%
a 1037431
 
9.9%
733786
 
7.0%
i 612885
 
5.8%
t 490496
 
4.7%
m 439604
 
4.2%
p 431719
 
4.1%
d 413868
 
3.9%
S 402546
 
3.8%
Other values (26) 3566228
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10512729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1214507
 
11.6%
e 1169659
 
11.1%
a 1037431
 
9.9%
733786
 
7.0%
i 612885
 
5.8%
t 490496
 
4.7%
m 439604
 
4.2%
p 431719
 
4.1%
d 413868
 
3.9%
S 402546
 
3.8%
Other values (26) 3566228
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10512729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1214507
 
11.6%
e 1169659
 
11.1%
a 1037431
 
9.9%
733786
 
7.0%
i 612885
 
5.8%
t 490496
 
4.7%
m 439604
 
4.2%
p 431719
 
4.1%
d 413868
 
3.9%
S 402546
 
3.8%
Other values (26) 3566228
33.9%

BRAND
Text

Distinct89
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.0 MiB
2024-09-25T06:46:43.073941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length17
Mean length8.1101076
Min length3

Characters and Unicode

Total characters7837608
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowMint
2nd rowMagenta
3rd rowBurgundy
4th rowIvory
5th rowUmber
ValueCountFrequency (%)
shoulders 107597
 
7.6%
hair 107597
 
7.6%
107597
 
7.6%
green 55054
 
3.9%
garnet 51482
 
3.7%
toothy 47412
 
3.4%
mint 43794
 
3.1%
blue 42376
 
3.0%
fuchsia 34059
 
2.4%
arctic 33926
 
2.4%
Other values (91) 778481
55.2%
2024-09-25T06:46:43.458160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 731404
 
9.3%
r 642306
 
8.2%
a 598243
 
7.6%
i 477346
 
6.1%
o 470321
 
6.0%
442975
 
5.7%
l 394044
 
5.0%
n 391404
 
5.0%
u 378063
 
4.8%
s 327239
 
4.2%
Other values (39) 2984263
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7837608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 731404
 
9.3%
r 642306
 
8.2%
a 598243
 
7.6%
i 477346
 
6.1%
o 470321
 
6.0%
442975
 
5.7%
l 394044
 
5.0%
n 391404
 
5.0%
u 378063
 
4.8%
s 327239
 
4.2%
Other values (39) 2984263
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7837608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 731404
 
9.3%
r 642306
 
8.2%
a 598243
 
7.6%
i 477346
 
6.1%
o 470321
 
6.0%
442975
 
5.7%
l 394044
 
5.0%
n 391404
 
5.0%
u 378063
 
4.8%
s 327239
 
4.2%
Other values (39) 2984263
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7837608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 731404
 
9.3%
r 642306
 
8.2%
a 598243
 
7.6%
i 477346
 
6.1%
o 470321
 
6.0%
442975
 
5.7%
l 394044
 
5.0%
n 391404
 
5.0%
u 378063
 
4.8%
s 327239
 
4.2%
Other values (39) 2984263
38.1%

Interactions

2024-09-25T06:46:32.987283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:26.976341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:28.184384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:29.335686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:30.434339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:31.768382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:33.170195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:27.198668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:28.383483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:29.521883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:30.621883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:31.977867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:33.361324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:27.378123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:28.571394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:29.712433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:30.816661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:32.179703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:33.590607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:27.556557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:28.757681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:29.890841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:30.994797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:32.405386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:33.814444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:27.798193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:28.957884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:30.070588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:31.242062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:32.600038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:34.041689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:28.001375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:29.154657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:30.251776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:31.436146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-25T06:46:32.796431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-25T06:46:43.555226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CATEGORYCOUNTYDISTRIBUTOR_CODEMNTH_CODEOC_CODESALES_PTR_VALUESALES_UNITSSALES_VALUESALES_VOLUMESTATESTREETSUBCATEGORY
CATEGORY1.0000.0910.0240.0260.0390.0080.0060.0080.0050.0400.0081.000
COUNTY0.0911.0000.0890.0380.0300.0080.0050.0080.0080.0800.0610.194
DISTRIBUTOR_CODE0.0240.0891.0000.0110.0140.0080.0050.0080.0070.0890.0330.071
MNTH_CODE0.0260.0380.0111.000-0.350-0.010-0.020-0.0080.0070.0180.0090.079
OC_CODE0.0390.0300.014-0.3501.0000.0060.046-0.007-0.0170.0130.0070.064
SALES_PTR_VALUE0.0080.0080.008-0.0100.0061.000-0.1260.9900.8940.0270.0050.012
SALES_UNITS0.0060.0050.005-0.0200.046-0.1261.000-0.1320.0670.0050.0050.012
SALES_VALUE0.0080.0080.008-0.008-0.0070.990-0.1321.0000.8850.0250.0050.011
SALES_VOLUME0.0050.0080.0070.007-0.0170.8940.0670.8851.0000.0180.0040.010
STATE0.0400.0800.0890.0180.0130.0270.0050.0250.0181.0000.0980.048
STREET0.0080.0610.0330.0090.0070.0050.0050.0050.0040.0981.0000.019
SUBCATEGORY1.0000.1940.0710.0790.0640.0120.0120.0110.0100.0480.0191.000

Missing values

2024-09-25T06:46:34.494606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-25T06:46:35.642109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MNTH_CODETRANS_DATESTART_DATESALES_VALUESALES_UNITSSALES_VOLUMESALES_PTR_VALUEOC_CODEDISTRIBUTOR_CODEOUTLET_CODECITYSTATECOUNTYSTREETPRODUCT_CODECATEGORYSUBCATEGORYBRAND
02023112023-11-102023-10-30142.86180.000432144.642857202211DB0209OL12036WauwatosaWisconsinScottStr3PRD0147Kids CareBaby CreamMint
12023112023-11-092023-10-30518.1830.001155518.181818202211DB0706OL49989HuntingtonWest VirginiaCity CenterStr3PRD0016Hair CareHair OilMagenta
22023112023-11-092023-10-30186.3610.000325186.363636202211DB0209OL112160Saint AugustineFloridaCity CenterStr2PRD0118SoapMedicated SoapBurgundy
32023112023-11-072023-10-301609.0930.0030001609.090909202211DB0209OL175188Redwood CityCaliforniaCity CenterStr5PRD0079Perfume and DeodrantsMale PerfumeIvory
42023112023-11-122023-10-30309.0910.000500309.090909202211DB0209OL80360KokomoIndianaCity CenterStr4PRD0080Perfume and DeodrantsUnisex PerfumeUmber
52023112023-11-122023-10-30142.86160.000272142.857143202211DB0655OL113196Jersey CityNew JerseyStephensStr4PRD0028SoapToilet SoapIndigo
62023112023-11-092023-10-30133.6430.000300133.636364202211DB0110OL11802BloomsburgPennsylvaniaCity CenterStr1PRD0095SoapMedicated SoapSea green
72023112023-11-022023-10-30254.5520.000500254.545455202211DB0652OL33490NarragansettRhode IslandCity CenterStr5PRD0107LotionBody LotionCoral
82023112023-11-092023-10-30214.29120.000780214.285714202211DB0706OL160013WinslowArizonaCity CenterStr2PRD0009DentalToothPasteToothy Coal
92023112023-10-312023-10-30133.93160.000384128.571429202211DB0652OL191004LufkinTexasOrangeStr2PRD0106Hair CareShampooHair & Shoulders
MNTH_CODETRANS_DATESTART_DATESALES_VALUESALES_UNITSSALES_VOLUMESALES_PTR_VALUEOC_CODEDISTRIBUTOR_CODEOUTLET_CODECITYSTATECOUNTYSTREETPRODUCT_CODECATEGORYSUBCATEGORYBRAND
9663902023112023-11-022023-10-30172.7310.000385172.727273202211DB0652OL175440SalemMissouriCity CenterStr3PRD0016Hair CareHair OilMagenta
9663912023112023-11-072023-10-301589.09160.0017121672.727273202211DB0209OL190869StamfordConnecticutCity CenterStr1PRD0153LotionBody LotionMustard
9663922023112023-11-152023-10-306480.001080.0181446750.000000202211DB0209OL222200HuntsvilleAlabamaCity CenterStr2PRD0086LotionHead LotionPeach
9663932023112023-11-232023-10-30134.29160.000416142.857143202211DB0209OL64632WestonWest VirginiaScottStr1PRD0069Perfume and DeodrantsFemale DeodrantArctic blue
9663942023112023-11-242023-10-3053.57120.00013253.571429202211DB0655OL81536WoodwardOklahomaScottStr2PRD0058SoapLiquid SoapRust
9663952023112023-11-162023-10-30268.57320.000832285.714286202211DB0110OL81665MattoonIllinoisCity CenterStr4PRD0069Perfume and DeodrantsFemale DeodrantArctic blue
9663962023112023-11-072023-10-30134.29160.000448142.857143202211DB0706OL65911VincennesIndianaCity CenterStr2PRD0094Perfume and DeodrantsUnisex PerfumeMocha
9663972023112023-11-112023-10-30134.42160.000448142.857143202211DB0110OL191975Yorba LindaCaliforniaCity CenterStr3PRD0094Perfume and DeodrantsUnisex PerfumeMocha
9663982023112023-11-212023-10-30202.8220.000214209.090909202211DB0706OL49926WatertownMassachusettsCity CenterStr1PRD0159Hair CareHair OilLily
9663992023112023-11-082023-10-30125.00160.000384128.571429202211DB0652OL65645RichmondKentuckyDolphinStr5PRD0147Kids CareBaby CreamMint

Duplicate rows

Most frequently occurring

MNTH_CODETRANS_DATESTART_DATESALES_VALUESALES_UNITSSALES_VOLUMESALES_PTR_VALUEOC_CODEDISTRIBUTOR_CODEOUTLET_CODECITYSTATECOUNTYSTREETPRODUCT_CODECATEGORYSUBCATEGORYBRAND# duplicates
02023092023-10-012023-08-288.9310.0000178.928571202209DB0209OL65494ShelbyvilleTennesseeSanta CruzStr5PRD0028SoapToilet SoapIndigo2
12023092023-10-012023-08-288.9320.0000228.928571202209DB0110OL112602Junction CityKansasSilverStr2PRD0105Perfume and DeodrantsFemale PerfumeGarnet2
22023092023-10-012023-08-288.9320.0000228.928571202209DB0110OL238594DallasTexasSpencerStr3PRD0105Perfume and DeodrantsFemale PerfumeGarnet2
32023092023-10-012023-08-288.9320.0000228.928571202209DB0110OL81664HattiesburgMississippiOrangeStr3PRD0105Perfume and DeodrantsFemale PerfumeGarnet2
42023092023-10-012023-08-2813.3930.00003313.392857202209DB0110OL144800Excelsior SpringsMissouriOrangeStr4PRD0105Perfume and DeodrantsFemale PerfumeGarnet2
52023092023-10-012023-08-2813.3930.00003313.392857202209DB0110OL222617IndianaPennsylvaniaDolphinStr4PRD0105Perfume and DeodrantsFemale PerfumeGarnet2
62023092023-10-012023-08-2813.3930.00003313.392857202209DB0110OL33285Fall RiverMassachusettsCity CenterStr3PRD0105Perfume and DeodrantsFemale PerfumeGarnet2
72023092023-10-012023-08-2813.3930.00003313.392857202209DB0110OL49488AllianceOhioDolphinStr3PRD0105Perfume and DeodrantsFemale PerfumeGarnet2
82023092023-10-012023-08-2817.8620.00004617.857143202209DB0652OL96577West CovinaCaliforniaCity CenterStr1PRD0027DentalToothPasteToothy Fresh2
92023092023-10-012023-08-2817.8640.00004417.857143202209DB0110OL80741GlenviewIllinoisScottStr2PRD0105Perfume and DeodrantsFemale PerfumeGarnet2